Recasting Self-Attention with Holographic Reduced Representations

• URL: http://arxiv.org/abs/2305.19534v1
• Date: Wed, 31 May 2023 03:42:38 GMT
• Title: Recasting Self-Attention with Holographic Reduced Representations
• Authors: Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt
• Abstract summary: Motivated by problems in malware detection, we re-cast self-attention using the neuro-symbolic approach of Holographic Reduced Representations (HRR) We obtain several benefits including $mathcalO(T H log H)$ time complexity, $mathcalO(T H)$ space complexity, and convergence in $10times$ fewer epochs. Our Hrrformer achieves near state-of-the-art accuracy on LRA benchmarks and we are able to learn with just a single layer.
• Score: 31.89878931813593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs can make using transformers infeasible. Motivated by problems in malware detection, where sequence lengths of $T \geq 100,000$ are a roadblock to deep learning, we re-cast self-attention using the neuro-symbolic approach of Holographic Reduced Representations (HRR). In doing so we perform the same high-level strategy of the standard self-attention: a set of queries matching against a set of keys, and returning a weighted response of the values for each key. Implemented as a ``Hrrformer'' we obtain several benefits including $\mathcal{O}(T H \log H)$ time complexity, $\mathcal{O}(T H)$ space complexity, and convergence in $10\times$ fewer epochs. Nevertheless, the Hrrformer achieves near state-of-the-art accuracy on LRA benchmarks and we are able to learn with just a single layer. Combined, these benefits make our Hrrformer the first viable Transformer for such long malware classification sequences and up to $280\times$ faster to train on the Long Range Arena benchmark. Code is available at \url{https://github.com/NeuromorphicComputationResearchProgram/Hrrformer}

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